A fusion of machine learning algorithms and traditional statistical forecasting models for analyzing American healthcare expenditure

Wang, John; Qin, Zhaoqiong; Hsu, Jeffrey; Zhou, Bin

doi:10.1016/j.health.2024.100312

Cited by 3 publications

(1 citation statement)

References 39 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The Framingham cardiovascular risk score, for example, was developed using Cox and logistic regression models on a large community‐based cohort to create a single, highly predictive covariate for cardiovascular outcomes. 16 Approaches that combine traditional statistical methods with ML have shown promise in identifying risk factors, 17 forecasting health expenditure, 18 and detecting life‐threatening infections in infants. 19 These hybrid models can often improve predictive accuracy while preserving interpretability and/or explainability, which has been a focus of regulators in their development of standards for trustworthy AI.…”

Section: The Utility Of Ai‐generated Dts In Clinical Trialsmentioning

confidence: 99%

Increasing acceptance of AI‐generated digital twins through clinical trial applications

Vidovszky,

Fisher,

Loukianov

et al. 2024

Clinical Translational Sci

View full text Add to dashboard Cite

Today's approach to medicine requires extensive trial and error to determine the proper treatment path for each patient. While many fields have benefited from technological breakthroughs in computer science, such as artificial intelligence (AI), the task of developing effective treatments is actually getting slower and more costly. With the increased availability of rich historical datasets from previous clinical trials and real‐world data sources, one can leverage AI models to create holistic forecasts of future health outcomes for an individual patient in the form of an AI‐generated digital twin. This could support the rapid evaluation of intervention strategies in silico and could eventually be implemented in clinical practice to make personalized medicine a reality. In this work, we focus on uses for AI‐generated digital twins of clinical trial participants and contend that the regulatory outlook for this technology within drug development makes it an ideal setting for the safe application of AI‐generated digital twins in healthcare. With continued research and growing regulatory acceptance, this path will serve to increase trust in this technology and provide momentum for the widespread adoption of AI‐generated digital twins in clinical practice.

show abstract

Section: The Utility Of Ai‐generated Dts In Clinical Trialsmentioning

confidence: 99%

Increasing acceptance of AI‐generated digital twins through clinical trial applications

Vidovszky,

Fisher,

Loukianov

et al. 2024

Clinical Translational Sci

View full text Add to dashboard Cite

show abstract

An Optimization Method for Short-Term Power Load Forecasting Based on IPSO-SVM

Shi,

Wang,

Huang

et al. 2024

2024 6th International Conference on Energy Systems and Electrical Power (ICESEP)

View full text Add to dashboard Cite

Robust Prediction of Healthcare Inflation Rate With Statistical and AI Methods in Iran

Shaibani,

Fazaeli

2024

Journal of Applied Mathematics

View full text Add to dashboard Cite

The expected healthcare (HC) inflation rate (IR) (HCIR) is an important variable for all economic agents within HC systems. In recent years, during the COVID‐19 pandemic, Iran has experienced a high HCIR in its health system. In this context, a robust approximation of HCIR will be a helpful tool for health authorities and other decision makers. Using monthly time series data of HCIR in Iran, we developed various forecasting techniques based on classical smoothing methods, decomposition ETS (error, trend, and seasonality) approaches, autoregressive (AR) integrated moving average (ARIMA), seasonal ARIMA (SARIMA), and a multilayer nonlinear AR artificial neural network (NARANN) with several training algorithms including Bayesian regularization (BR), Levenberg–Marquardt (LM), scaled conjugate gradient (SCG), Broyden–Fletcher–Goldfarb–Shanno (BFGS) quasi‐Newton, conjugate gradient with Powell–Beale restarts (CGB), conjugate gradient with Fletcher–Reeves updates (CGF), and resilient propagation (RPROP) algorithms. Initially, based upon various criteria and possible combinations, we selected the superior model for each method separately. After that, the best model in each category is involved in 6‐ and 12‐multi‐step‐ahead prediction. In this stage, several error criteria are calculated. According to our findings, in a six‐step forecasting window, the Holt–Winters with a multiplicative seasonal pattern and SARIMA showed less bias, though compared to other alternatives like NARANN‐lm/br, the difference was relatively small. In the next process, by doubling the forecasting window, it is observed that artificial neural network (ANN) (i.e., Bayesian NARANN) strictly outperformed other models. As a result, in shorter steps, the Holt–Winters method can provide a better prediction, while in longer windows, Bayesian NARANN can be implemented vigorously for the prediction. Finally, we used 10 models to predict the future trend of HCIR in Iran till the end of July 2024.

show abstract

A fusion of machine learning algorithms and traditional statistical forecasting models for analyzing American healthcare expenditure

Cited by 3 publications

References 39 publications

Increasing acceptance of AI‐generated digital twins through clinical trial applications

Increasing acceptance of AI‐generated digital twins through clinical trial applications

An Optimization Method for Short-Term Power Load Forecasting Based on IPSO-SVM

Robust Prediction of Healthcare Inflation Rate With Statistical and AI Methods in Iran

Contact Info

Product

Resources

About